We propose a method for clustering sets of vectors by packing spheres learnt to represent the support of the different sets. The algorithm can work efficiently in a kernel-induced feature space by using the kernel trick. Experimental results on synthetic and real-world datasets show that the proposal is competitive with the state of the art.